Measurement masks to reconstruct x-ray spectra

ABSTRACT

An X-ray imaging system for reconstructing X-ray spectra includes an integrating detector and a measurement mask, including at least one physical filter, positioned between the integrating detector and an X-ray source spectrum. The integrating detector receives a masked X-ray spectrum after the source spectrum has been filtered in accordance with the measurement mask. As a result of the measurement mask containing one or more physical filters being combined, a measurement mask having energy band-pass regions can be generated, to cover the source spectrum. Measured data, based on the masked X-ray spectrum and characteristics of the measurement mask, is collected from the integrating detector. The X-ray imaging system reconstructs an X-ray spectrum and generates the reconstructed X-ray spectrum based on applying a predetermined algorithm, such as total variation minimization reconstruction, to the measured data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Nonprovisional applicationSer. No. 16/364,389 entitled “Reconstruction of X-ray Spectra UsingIntegrating Detectors (RoXS-ID),” filed on Mar. 26, 2019; the disclosureof which is incorporated herein by reference in its entirety.

STATEMENT OF GOVERNMENT INTEREST

The present invention was made by one or more employees of the UnitedStates Department of Homeland Security in the performance of officialduties.

BACKGROUND

The “background” description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description which may nototherwise qualify as prior art at the time of filing, are neitherexpressly or impliedly admitted as prior art against the presentinvention.

X-ray imaging modalities employ bremsstrahlung sources that emit photonsover a wide and continuous energy range. Energy-sensitive X-raydetectors can lack spatial resolution and have poor energy resolution.Also, for energy-sensitive detectors that do exhibit high energyresolution, the higher resolution comes with tradeoffs, because suchdetectors often need to be cooled significantly to reduce thermal noisewhich adds to the complexity and expense of implementation.

SUMMARY

The foregoing paragraphs have been provided by way of generalintroduction, and are not intended to limit the scope of the followingclaims. The described embodiments, together with further advantages,will be best understood by reference to the following detaileddescription taken in conjunction with the accompanying drawings.

According to aspects of the disclosed subject matter, a system forreconstructing X-ray spectra can include an X-ray imaging system. TheX-ray imaging system can include an integrating detector and ameasurement mask, including at least one physical filter, positionedbetween an X-ray source and the integrating detector. The physicalfilters can be combined, e.g., randomly, to allow a large range of X-rayenergies to pass. The measurement mask enables the integrating detectorto receive a masked X-ray spectrum after passing through the measurementmask. As a result of the measurement mask containing one or morephysical filters being combined, a measurement mask with wide energyband-pass regions can be generated. Measured data can then be collectedfrom the integrating detector, based on the masked X-ray spectrum andthe measurement mask. The X-ray imaging system can reconstruct an X-rayspectrum and generate the reconstructed X-ray spectrum based on applyinga predetermined algorithm, such as total variation minimizationreconstruction, to the collected data.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 depicts an example overview of a system for reconstructing X-rayspectra using an integrating detector according to one or more aspectsof the disclosed subject matter;

FIG. 2 depicts an example overview of a measurement mask for use with anintegrating detector according to one or more aspects of the disclosedsubject matter;

FIG. 3 depicts an example overview of a layered sub-pixel measurementmask for use with an integrating detector according to one or moreaspects of the disclosed subject matter;

FIG. 4 depicts an example computational tungsten X-ray spectrumaccording to one or more aspects of the disclosed subject matter;

FIG. 5 depicts a log-log plot of the mass attenuation coefficient as afunction of X-ray energy for aluminum, tin, and iron according to one ormore aspects of the disclosed subject matter;

FIG. 6 depicts a first spatial derivative (gradient) of a tungsten X-rayspectrum according to one or more aspects of the disclosed subjectmatter;

FIG. 7 depicts an example randomly generated measurement mask accordingto one or more aspects of the disclosed subject matter;

FIG. 8 depicts an example simulated measured data based on compressivesensing according to one or more aspects of the disclosed subjectmatter;

FIG. 9 depicts an example result of total variation minimization signalreconstruction according to one or more aspects of the disclosed subjectmatter;

FIG. 10 depicts an example measurement mask including wide band-passregions according to one or more aspects of the disclosed subjectmatter;

FIG. 11 depicts an example reconstructed spectrum using a wide band-passmeasurement mask according to one or more aspects of the disclosedsubject matter;

FIG. 12 is a flow chart of a method for reconstructing X-ray spectrausing an integrating detector according to one or more aspects of thedisclosed subject matter;

FIG. 13 is a flow chart of a method for identifying a configuration fora measurement mask according to one or more aspects of the disclosedsubject matter; and

FIG. 14 is a hardware block diagram of a server according to one or moreexample aspects of the disclosed subject matter.

DETAILED DESCRIPTION

The description set forth below in connection with the appended drawingsis intended as a description of various embodiments of the disclosedsubject matter and is not necessarily intended to represent the onlyembodiment(s). In certain instances, the description includes specificdetails for the purpose of providing an understanding of the disclosedsubject matter. However, it will be apparent to those skilled in the artthat embodiments may be practiced without these specific details. Insome instances, well-known structures and components may be shown inblock diagram form in order to avoid obscuring the concepts of thedisclosed subject matter.

Reference throughout the specification to “one embodiment” or “anembodiment” means that a particular feature, structure, characteristic,operation, or function described in connection with an embodiment isincluded in at least one embodiment of the disclosed subject matter.Thus, any appearance of the phrases “in one embodiment” or “in anembodiment” in the specification is not necessarily referring to thesame embodiment. Further, the particular features, structures,characteristics, operations, or functions may be combined in anysuitable manner in one or more embodiments. Further, it is intended thatembodiments of the disclosed subject matter can and do covermodifications and variations of the described embodiments.

It must be noted that, as used in the specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the context clearly dictates otherwise. That is, unless clearlyspecified otherwise, as used herein the words “a” and “an” and the likecarry the meaning of “one or more.” Additionally, it is to be understoodthat terms such as “left,” “right,” “top,” “bottom,” “front,” “rear,”“side,” “height,” “length,” “width,” “upper,” “lower,” “interior,”“exterior,” “inner,” “outer,” and the like that may be used herein,merely describe points of reference and do not necessarily limitembodiments of the disclosed subject matter to any particularorientation or configuration. Furthermore, terms such as “first,”“second,” “third,” etc., merely identify one of a number of portions,components, points of reference, operations and/or functions asdescribed herein, and likewise do not necessarily limit embodiments ofthe disclosed subject matter to any particular configuration ororientation.

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views.

FIG. 1 depicts an example embodiment of an overview of an X-ray spectrareconstruction system 100 using an integrating detector (herein referredto as system 100) according to one or more aspects of the disclosedsubject matter. The system 100 can include an imaging system 105connected to a remote device 120 and a server 125 via a network 130. Theimaging system 105 can be an X-ray imaging system, for example. Theimaging system 105 can include an integrating detector 110 and ameasurement mask 115, which may include one or more physical filters116. For X-ray imaging, integrating detectors can be used in nearlyevery application. Integrating detectors sum the energy of incomingphotons for each pixel to form an image. Summing the energy results in aloss of energy-dependent information. Medical imaging and contrabanddetection technologies stand to benefit from energy-dependentinformation, otherwise lost by integrating detectors, because access toX-ray photon energy dependent information can improve the determinationof the properties of materials being imaged, as well as the inherentquality of the images being produced.

In the example embodiment, the measurement mask 115 includes one or morephysical filters 116 placed between an X-ray source spectrum (not shownin FIG. 1) and one or more sections of the integrating detector panel,as further described herein. An item to be imaged also is placed betweenthe source spectrum and the integrating detector panel. In an exampleembodiment, the measurement mask 115 can be positioned in the imagingsystem 105 to be directly exposed to the source spectrum (i.e.,measurement mask 115 positioned between the source spectrum and the itemto imaged). In an example embodiment, the measurement mask 115 can bepositioned in the imaging system 105 to directly exposed output from themeasurement mask 115 to the integrating detector 110 (i.e., measurementmask 115 positioned between the item to imaged and the integratingdetector 110). The imaging system 105 includes processing circuitry toassist in the operation of and/or to independently operate the system100. The measurement mask 115 can be constructed as a mesh of filters,such as a selection of metal foils, mounted in a frame that is alignedwith the integrating detector 110. In examples, the measurement mask 115can be constructed by depositing/bonding filter materials onto ascintillator of the imaging system 105.

The server 125 can represent one or more servers connected to theimaging system 105 and the remote device 120 via the network 130. Theserver 125 can include processing circuitry to perform variousprocessing for the system 100 as further described herein. In anexample, the server 125 receives requests from one or more of theimaging system 105 and the remote device 120 via the network 130.Additionally, the server 125 can transmit information to the imagingsystem 105 and the remote device 120 via the network 130.

The remote device 120 can represent one or more remote devices connectedto the imaging system 105 and the server 125 via the network 130. Theremote device 120 can be a computer, a laptop, a tablet, a smart phone,a PDA, and the like. The remote device can include processing circuitryto assist in operating the system 100. The remote device 120 can includean interface, such as a digital and/or physical keyboard and/or a mouseand/or touch-based input functionality, allowing users to interact withfunctionality of the imaging system 105, for example.

One or more of the imaging system 105, the remote device 120 and theserver 125 can include one or more local storage components to storevarious information, input, and/or output related to the operation ofthe system 100. An independent database can be accessible via thenetwork 130.

The network 130 can represent one or more networks connecting theimaging device 105, the remote device 120, and the server 125. Thenetwork 130 can be a public network, such as the Internet, or a privatenetwork such as an LAN or WAN network, or any combination thereof andcan also include PSTN or ISDN sub-networks. The network 130 can also bewired, such as an Ethernet network, or can be wireless such as acellular network including EDGE, 3G 4G, and LTE/LTE-A wireless cellularsystems. The wireless network can also be Wi-Fi, Bluetooth, or any otherwireless form of communication that is known.

FIG. 2 depicts an example overview of a measurement mask 220 for usewith an integrating detector 230 according to one or more aspects of thedisclosed subject matter. A source spectrum 210 is emitted toward theintegrating detector 230, filtered by the measurement mask 220, andreceived by the integrating detector 230 as a masked spectrum. Thefiltered spectrum is filtered in accordance with the measurement mask220 and falls onto the integrating detector 230 as a masked spectrum.The measurement mask 220 specifies, e.g., in energy-intensity space,what energies of photons are being allowed to pass through the variousfilters, such that the measurement mask does not necessarily map thephysical location(s) of filter(s).

The integrating detector 230 is illustrated for the sake of simplicityas having 8 pixels, and the figures are not shown to scale (a detectorpixel would be on the order of 1 millimeter square, and the pixels ofthe integrating detector 230 can be arranged along a curved arc).Similarly, the measurement mask 220 is illustrated as a grid of 8materials, arranged to correspond to the pixels of the integratingdetector on a 1:1 basis. Accordingly, the measurement mask 220 filtersthe source spectrum 210 by a single material per pixel of theintegrating detector 230. The measurement mask 220 can include opensections, to allow unfiltered source spectrum 210 to pass throughunfiltered when the source spectrum is filtered in accordance with themeasurement mask 220. The location of the different filter materialswithin the measurement mask can be arranged randomly, or according to amathematical pattern. For example, the mathematical combinations ofdifferent filter materials can be calculated, for those materialssuitable for analysis of a given X-ray source spectrum. The filtermaterials can then be laid out in the grid of the measurement maskaccording to the mathematical combinations. In an example embodiment,all possible combinations of the materials can be generated, with eachcombination being implemented once in the measurement mask. A subset ofthe possible filter materials can be used, without needing to use allmaterials, by determining an appropriate threshold level of resolution,and eliminating those materials that are not necessary to generatingthat threshold level (e.g., omit those materials that merely serve tosmooth out the reconstructed spectrum, in view of other materials). Inan example, the filter materials can be chosen and arranged on apseudo-random basis, e.g., based on an algorithm using a random numbergenerator to identify the choice of filter materials and the gridlocation for the various filter materials. As shown in FIG. 2, fivedifferent filter materials (including “open” which represents no filtermaterial) are arranged randomly in the eight possible grid locations forthe illustrative measurement mask 220.

The measurement mask 220 is shown, for the sake of illustration,positioned approximately midway between the source spectrum 210 and theintegrating detector 230. The measurement mask 220 can be positionedcloser to the source spectrum 210 or the integrating detector 230. In anexample embodiment, the measurement mask 220 can be coupled to thesource spectrum 210, e.g., deposited as metal layers directly onto asurface of an X-ray source tube or imaging system scintillator. In anexample embodiment, the measurement mask 220 can be coupled to theintegrating detector 230, e.g., as an overlay frame mounted onto theintegrating detector. In an example embodiment, the measurement mask 220is positioned, relative to a section of the imaging system for receivingan item to be imaged, such that the measurement mask 220 will directlyreceive the source spectrum 210 (e.g., positioned upstream of the itemto be imaged). In an example embodiment, the measurement mask 220 ispositioned downstream of the item to be imaged, such that theintegrating detector 230 will directly receive the masked spectrumpassing through the measurement mask 220.

The measurement mask 220 can be made of thin foils of different metalshaving various X-ray attenuation properties, e.g., according toabsorption edges that can be used to span the energies in the sourcespectrum, as described in further detail below. Example filter materialsinclude tin, copper, titanium, indium, nickel, iron, gold, and othermaterials, which can be identified and chosen based on variouscharacteristics, such as their X-ray absorption edges, in view ofbaseline attributes of the X-ray source. The measurement mask 220 can beconstructed in various ways, such as by using thin foils with sufficientthickness to maintain structural integrity when supported by a frame.The measurement mask can be constructed of transparent material that hasbeen selectively coated or sputtered with metal filter materialscorresponding to grid sections of the transparent material. In anexample, the measurement mask 220 can be formed by directly coating asection of the imaging system, e.g., by depositing thin films of metalsonto a scintillator of an X-ray source.

FIG. 3 depicts an example overview of a layered sub-pixel measurementmask for use with an integrating detector according to one or moreaspects of the disclosed subject matter. The source spectrum 310 isemitted toward the integrating detector 330, and filtered by two layersof the measurement mask 320 (illustrated in an exploded view forclarity). The filtered spectrum is filtered in accordance with themeasurement mask 320 and falls onto the integrating detector 330 as amasked spectrum.

The integrating detector 330 is illustrated for the sake of simplicityas having 8 pixels. Similarly, a first layer of the measurement mask320A is illustrated as a grid of 8 materials, arranged to correspond tothe pixels of the integrating detector on a 1:1 basis. A second layer ofthe measurement mask 320B is illustrated as a grid of 32 materials,arranged to correspond to the pixels of the integrating detector on a4:1 basis according to a sub-pixel basis. Accordingly, the measurementmask 320 filters the source spectrum 310 by multiple materials,corresponding to each pixel of the integrating detector 330. In anexample embodiment, a pixel of the integrating detector 330 can receiveunfiltered source spectrum 310, by aligning one open grid position ofmeasurement mask 320A with four corresponding open grid positions ofmeasurement mask 320B.

In other embodiments, the sub-pixel layer of measurement mask 320B canbe used alone, without the measurement mask 320B. Additional layers,with varying pixel densities, can be stacked and used in the measurementmask. In various embodiments, a layer can include larger grid sizes toallow a single material to cover/overlap multiple pixels of theintegrating detector. A layer can include smaller grid sizes, e.g.,having sub-pixels on a 9:1, 16:1, or other ratios relative to the pixelsize of the integrating detector. In an embodiment, the measurement maskcan be configured to enable a combination of all available filtermaterials relevant to a given X-ray source to cover a given pixel of theintegrating detector.

The mathematical approach for analyzing collected measurements can bevaried in accordance with the type of measurement mask. For example, inembodiments where each pixel of the integrating detector is covered byno more than one filter material, the collected data from multipledetector pixels can be combined, e.g., to convolve together differentones of the various detector pixels' collected data. Accordingly, suchinformation about the type and configuration of the measurement mask(type of filter material(s), grid positions of those materials, gridsize, number of layers, and so on) can be provided to a computing systemto ensure an appropriate mathematical approach is performed on collecteddata, in order to apply a reconstruction algorithm using, e.g., ameasurement matrix having values corresponding to grid positions andfilter materials/attenuations. Such metrics corresponding to a givenmeasurement mask can be assigned a mask identification, which can becommunicated to a computing system (either manually by an operator, orautomatically by the computing system recognizing an identificationassociated with the mask (barcode, RFID, mechanical keying, and thelike). In an example embodiment, different types of measurement maskconfigurations can be assigned corresponding mask reference numbers,which can be coded into the masks for automatic electronicidentification by the imaging system to which the coded measurement maskis fitted, thereby enabling the imaging system to automatically apply amathematical reconstruction approach that is appropriate for thatspecific measurement mask configuration (e.g., identify characteristicsof the measurement mask which translate into a measurement matrix to beused in a reconstruction algorithm).

FIG. 4 depicts an example computational tungsten X-ray spectrum 400according to one or more aspects of the disclosed subject matter. Thecomputational tungsten X-ray spectrum 400 is an example of acomputationally modeled bremsstrahlung X-ray radiation spectrum. Alsopresent in the emission spectra of bremsstrahlung X-ray sources arefluorescence photons that are characteristic of the electron energylevels of the source's anode material. For example, with reference toFIG. 4, tungsten anode fluorescence photons are represented by intensepeaks around 60 keV and 70 keV. The various embodiments described hereinare capable of working with other types of X-ray sources, withcorresponding baseline characteristic X-ray source spectra. For example,embodiments can also operate with X-ray sources based on molybdenum,copper, silver, gold, rhodium, rhenium, graphite, iron, and other X-rayanode materials, e.g., those suitable for non-destructive evaluation.

The illustrated peaks, and other characteristics of the source spectrum400 can change or have long-term drift over time, e.g., with repeatedusage of the X-ray source. The example embodiments and approachesdescribed herein enable an imaging system to identify real-time feedbackon whether the source spectrum is changing, including changes related tohigh-voltage supply fluctuations, physical changes in the structureand/or stability of the X-ray source (e.g., anode pitting), changes inpower output and/or shifts in mean energy location, the occurrence ofbeam hardening, gas infiltration into the X-ray tube, and other changesthat can occur in a given imaging system. Accordingly, the embodimentsdescribed herein provide additional benefits, in addition to increasedenergy spectrum and spatial resolution imaging benefits, such asidentifying problems developing with the imaging system, changes instability of imaged explosives including rapidly evolving homemadeexplosives (HMEs), details of images in medical applications, when itmay be time to perform repairs to the imaging system, when an X-ray tubemay need to be re-aligned, serviced, or replaced, and other benefitsrelated to identifying changes in the source spectrum as enabled by theembodiments described herein.

FIG. 5 depicts a log-log plot of the mass attenuation coefficient as afunction of X-ray energy for aluminum, tin, and iron according to one ormore aspects of the disclosed subject matter. Tin and other materials(including filter materials not specifically illustrated, such as indiumand the like) are example materials that can serve as a filter, e.g., byusing a thin film of tin that can be combined with one or more otherfilter materials to form a measurement mask 115. In order to accessenergy-dependent information with existing X-ray imaging infrastructurethat otherwise lacks spatial/energy resolution, a new technique isproposed called Reconstruction of X-ray Spectra from IntegratingDetectors (RoXS-ID). RoXS-ID can utilize various physical filters 116that are placed in front of one or more portions of the integratingdetector 110. The various physical filters 116 can have enhancedabsorption edges that are at X-ray energies spanning the range of X-rayenergies produced by the source. For example, FIG. 5 illustrates tin(Sn) having absorption edges 505 and 510, aluminum (Al) havingabsorption edge 515, and iron (Fe) having absorption edge 520. Byaggregating the measurements that result from the use of a measurementmask with many filter combinations having absorption edges at differentenergies, the masked X-ray spectrum can be recovered by an integratingdetector using a mathematical optimization technique called totalvariation minimization. In an example combination, a film of Al can beoverlaid with a film of Fe, and the resulting combination provides acombination having a wide bandpass corresponding to the differencebetween the absorption edge 515 of Al and the absorption edge 520 of Fe.

More specifically, filter materials can be selected to create a “notch”between two absorption edges, corresponding to a difference inabsorption. The two values corresponding to the absorption edges can besubtracted, and the remaining contrast will correspond to the energy andat the difference between the absorption edges. This approach creates anenergy band for the measurement mask. To get additional resolution,additional filter materials are used, with corresponding differences inabsorption edges, to sufficiently cover/span the energy space of thesource spectrum to enable enough collected data of the masked spectrumfor the reconstruction algorithm to reconstruct a representation of thesource spectrum (i.e., reconstructed spectrum) to a desired level ofresolution and/or convergence of the reconstructed spectrum.

Thus, the selection, and/or arrangement, of the various filter materialscan be determined based on the expected baseline energy levels involvedin the source spectrum (e.g., by identifying what type of X-raysource/tube will be involved, such as an X-ray tube based on tungstenwith its characteristic energy levels), and the selection of filtermaterials to provide k-edges to address the characteristic baselineenergy levels of the X-ray source, and the selection of filter materialsto, in aggregate, cover a range of the baseline source spectrum.Accordingly, by selecting filter materials of the measurement mask toaddress the characteristic baseline energy levels expected for a giventype of X-ray (e.g., tungsten based X-ray source), the exampleembodiments can obtain masked spectra that is filtered by themeasurement mask to show finer details of the source spectrum, such asdetails illustrating a deviation from the baseline levels, to revealadditional details, such as degradation of the X-ray source, beamhardening, misalignment, or other finer details of a given X-ray source,in addition to identifying a type of the baseline X-ray source type.

FIG. 6 depicts a first spatial derivative (gradient) of a tungsten X-rayspectrum 600 according to one or more aspects of the disclosed subjectmatter. RoXS-ID was inspired by compressive sensing. Compressive sensingwas developed in an attempt to circumvent the restrictions on signalsampling imposed by the Shannon-Nyquist sampling theorem.

Compressive sensing exploits the fact that a signal that is dense in onebasis may be sparse in another basis. A sparse signal has very fewnon-zero (or nearly non-zero) amplitude coefficients. For example, thespectrum 400 in FIG. 4 in the energy-intensity basis contains manynon-zero values across the energy range. However, if the spectrum 400 istransformed by taking its first spatial derivative, as show in FIG. 6,the signal becomes sparse. Only a small fraction of the transformedamplitudes are large with most being at or near zero. The sparsity ofthe transformed X-ray spectrum implies that concepts from compressivesensing, particularly signal reconstruction methods, may be applicable.Sets of measurements at random energies can capture information aboutthe signal in the sparse basis. Repeating many sets of these randommeasurements can provide enough information about the signal in thesparse basis to reconstruct the original signal (e.g., the X-rayspectrum 400).

FIG. 7 depicts an example randomly generated measurement mask 700according to one or more aspects of the disclosed subject matter. Theillustrated depiction of measurement mask 700 represents the state ofmeasurement (white=pass, black=block) for each energy bin of interest,where an energy bin corresponds to a narrow range of energy values. Inother words, white areas represent X-ray energies that will be allowedto pass, while black areas represent blocked X-ray energies, for a givenmeasurement number (repeated 500 times). In this example, 500measurement trials were coded into the mask as represented by the y-axismeasurement numbers. For each measurement number (row), a combination ofhypothetical filters is applied by the measurement mask 700 toselectively block or pass the original signal as indicated by the blackand white pixels, across the variously indicated energy bins for thatrow. The randomly generated measurement mask 700 may represent a levelof precision that does not correspond to physically achievablecombinations of metal foils to serve as filters, but is used toillustrate the techniques described herein as applied to fullypseudorandom combinations of passed/masked energy bins across 500measurements.

FIG. 8 depicts an example simulated masked measured data 800 based oncompressive sensing according to one or more aspects of the disclosedsubject matter. Modeling results can be based on determining g, themasked measured data 800, according to the following equation:

$\begin{matrix}{g = { {Mf}arrow\begin{bmatrix}{g1} \\\vdots \\{gn}\end{bmatrix}  = {\begin{bmatrix}M_{11} & \ldots & M_{k1} \\\vdots & \ddots & \vdots \\M_{1n} & \ldots & M_{kn}\end{bmatrix}\begin{bmatrix}{f1} \\\vdots \\{fk}\end{bmatrix}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

The masked measured data 800, g, is represented as the inner product ofa measurement matrix, M, and a signal represented by a column vector.Using equation 1 with the spectrum 400 from FIG. 4 as f, and themeasurement mask 700 shown in FIG. 7 as the measurement matrix M, theresulting masked measurement 800, g, is shown in FIG. 8. The appearanceof the masked measurement 800 seems random, which corresponds to thepseudo random nature of the measurement mask 700 used to generate themasked measurement 800.

FIG. 9 depicts an example result of total variation minimization signalreconstruction according to one or more aspects of the disclosed subjectmatter. Using the masked measured data 800 represented in FIG. 8 as theinput to a total variation minimization reconstruction algorithm resultsin the reconstructed X-ray spectrum shown in FIG. 9. Further details ofan example total variation minimization signal reconstruction approachcompatible with the example embodiments described herein can be found inE. Sidky, et. al. “Accurate image reconstruction from few-views andlimited-angle data in divergent-beam CT,” Journal of X-ray Science andTechnology 14 (2006) 119-139, the contents of which are herebyincorporated by reference.

Fifty iterations of an embodiment of a total variation minimizationalgorithm were performed. The example embodiment of the algorithm iscapable of producing results that, with each iteration, converge closerto a target result within a desirable standard deviation. The result isnearly an exact replication of the original spectrum 400 from FIG. 4.The measurement mask 700 shown in FIG. 7 represents an example of onetype of ideal measurement mask, which is capable of achieving a wideenergy band-pass (e.g., 1 keV), with the center of the band-passregion(s) being easily selectable based on ideal mathematical featuresthat are not limited by physically available materials. In practice, byvarying possible combinations of available physical filters to produceexample measurement masks, a finite number of possible physicalmaterials available for use (e.g., foils of different types of metals)can be used, whose finite number of possible combinations results in afinite number of possible bandpass filter combinations which thereforeinfluences the masked spectrum produced when a source spectrum isfiltered according to the example measurement masks.

In an example, a practical physical measurement mask is based on, e.g.,filter combinations of physical filters 116 that can be configured andarranged to form a measurement mask, such that a range of X-ray energiesare allowed to pass by the various different combinations of individualfilter materials each having its own absorption edge(s) and/orattenuation characteristics as illustrated, e.g., in FIG. 5. Bycombining multiple filter materials into different, e.g., pseudo-randomcombinations to create different wide energy bandpass regions in themeasurement mask, the original spectrum can be reconstructed from maskedmeasurement data obtained by a detector (e.g., integrating detector)implementing the measurement mask. An example of a wide band-passmeasurement mask based on combinations of different available existingphysical filters is shown in FIG. 10.

FIG. 10 depicts an example measurement mask 1000 including wideband-pass regions according to one or more aspects of the disclosedsubject matter. Each measurement number corresponds to application ofone or more physical filters, such that the one or more physical filtersselectively blocks and passes the different energy bins. As illustrated,many of each of the measurement numbers of the measurement mask includea wide portion of white, illustrating a wide band pass for thatcombination of filter(s), with various sections of energy bins (e.g.,approximately the lower energy bins 0-30, with various other higherenergy bins) being blocked. Such passing and blocking behavior for eachmeasurement number can be accomplished by using corresponding physicalfilter materials, based on the respective absorption edges and filteringbehavior as explained above. The resulting masked spectrum 1100represented by FIG. 11, filtered in accordance with the measurement mask1000, depicts results that have been reconstructed from masked measureddata as obtained by a practically achievable measurement mask such asmeasurement mask 1000.

FIG. 11 depicts an example reconstructed spectrum 1100 using a wideband-pass measurement mask according to one or more aspects of thedisclosed subject matter. As seen by the resulting spectrum 1100, ameasurement mask containing wide band-pass regions (e.g., measurementmask 1000) can be used to filter an original X-ray spectra emittedtoward the detector, enabling the detector to receive masked measureddata filtered in accordance with the measurement mask that can bereconstructed to reveal spatial/energy resolution of the original X-rayspectra.

As a result of the system 100 using the measurement mask andreconstruction approach, the system 100 includes several advantages. Forexample, the system 100 includes improved spatial resolution and energyresolution. Additionally, contrary to other high energy resolutionsystems that need to be cooled significantly to reduce thermal noise,the system 100 reduces the need to be cooled significantly, therebyreducing complexity and expense of implementation.

FIG. 12 is a flow chart of a method for reconstructing X-ray spectrausing an integrating detector according to one or more aspects of thedisclosed subject matter. In block 1210, an imaging system filters asource spectrum in accordance with a measurement mask including at leastone physical filter to provide one or more wide energy band-passregions. The measurement mask is positioned between an integratingdetector and a source of the imaging system to filter the sourcespectrum into a masked spectrum that is directed toward the integratingdetector. For example, the measurement mask can be supported by a framebetween the detector and source, applied to a scintillator at a sourceof the imaging device, or can be mounted to a detector of the imagingdevice, or a combination of locations. The measurement mask canincorporate various different filter materials to cover the baselineX-ray energy of the source spectrum.

In block 1220, measured data is collected from the integrating detector,corresponding to the masked spectrum. The measured data is generated bythe integrating detector, after the source spectrum has been filtered inaccordance with the measurement mask and received as masked spectrum atthe integrating detector.

In block 1230, a reconstructed spectrum of the source spectrum isgenerated, based on applying total variation minimization reconstructionto the measured data of the masked spectrum using values and positionsof the at least one physical filter forming the measurement mask. Forexample, the imaging system can include processing circuitry and/orintegrated computing system, or can provide data for processing by aseparate computing system (e.g., remote device 120 and/or server 125),to iteratively solve a total variation minimization reconstructionalgorithm, based on measurement values corresponding to a matrix offilter positions and attenuation values of the various filter materialsof the measurement mask, to achieve convergence of the reconstructedspectrum.

The total variation minimization reconstruction algorithm can beiteratively optimized/solved by assuming an initial estimate for f e.g.,based on baseline expected spectrum of a given type of X-ray source. Thealgorithm can then be iterated by updating the initial estimate based onsolving the algorithm, e.g., using equation 1 and information such asthe collected measured data g, the measurement values of the measurementmask M₁₁ . . . M_(kn), and the current estimated solution for f which isthen updated by having performed this iteration. The iterative solutionproceeds until the current estimated solution converges. Convergence ofthe solution can be checked iteratively, e.g., whether there is anarbitrarily small difference between the solution of the currentiteration and the solution of the previous iteration (e.g., based on anabsolute value, or based on a relative value such as a percentage),whether the solutions converge to within a standard deviation,dynamically sampling different areas of the reconstructed spectrum tocheck for standard deviations in the sampled sections as a measure ofhow jittery or straight that section might appear as a measure forconvergence, whether a given number of iterations have been performed(e.g., 50, or 500, etc.), whether a given amount of processing time haspassed, or other approaches to check for convergence of the iterativeprocess.

FIG. 13 is a flow chart of a method for identifying a configuration fora measurement mask according to one or more aspects of the disclosedsubject matter.

In block 1310, a type of X-ray source used in the imaging systemproducing the source spectrum is identified. For example, a processingsystem can identify what kind of X-ray tube (e.g., tungsten-based) isused in the imaging system, and what baseline characteristics areassociated with that type of X-ray tube, including the baseline sourcespectrum. In an example embodiment, the imaging system identifies abrand and model number of the X-ray tube, and/or a technician inputs anidentification of the X-ray tube into the imaging system.

In block 1320, a plurality of physical filters is identified, to providea plurality of respective corresponding wide energy band-pass regions.For example, the baseline characteristics of the X-ray source can beused to identify which physical filter materials have absorptionenergies that can be used in aggregate to produce band-pass regionswhich sufficiently cover the baseline source spectrum of the X-raysource.

In block 1330, a configuration of the plurality of filters is identifiedthat, when combined into the measurement mask, achieve an aggregate wideenergy band-pass region(s) to sufficiently cover energies of the sourcespectrum. For example, the filters can be combined according to pseudorandom combinations, to achieve wide energy band-pass regions as aresult of the random combinations of the physical filters. Thecombination can be chosen to selectively omit some grids of themeasurement mask, to allow pass-through of unfiltered source spectrum inthe filtering of the source spectrum in accordance with the measurementmask. The combination also can be chosen such that there are gaps in thecovered energies, such that the aggregate coverage is sufficient byvirtue of iterative application of a reconstruction algorithm that canproduce sufficient convergence of the reconstructed spectrum based onthe purpose(s) which the reconstructed spectrum will serve (e.g.,sufficient for a desired resolution level based on a type of item to bescanned). For example, the reconstructed signal 900 shown in FIG. 11 issufficient for recognition of HMEs, and such a resolution can beachieved without fully covering the entire energy spectrum of the X-raysource. Accordingly, sufficient coverage can be viewed in terms of thegiven application of the reconstructed spectrum.

FIG. 14 is a hardware block diagram of the server 125 according to oneor more example aspects of the disclosed subject matter. It should beappreciated that the hardware block diagram of FIG. 14 can alsocorrespond to the imaging system 105 and/or the remote device 120. Next,a hardware description of the server 125 according to exampleembodiments is described with reference to FIG. 14. In FIG. 14, theserver 125 includes a CPU 1100 which performs the processes describedabove/below. The process data and instructions may be stored in memory1102. These processes and instructions may also be stored on a storagemedium disk 1104 such as a hard drive (HDD) or portable storage mediumor may be stored remotely. Further, the claimed advancements are notlimited by the form of the computer-readable media on which theinstructions of the inventive process are stored. For example, theinstructions may be stored on CDs, DVDs, in FLASH memory, RAM, ROM,PROM, EPROM, EEPROM, hard disk or any other information processingdevice with which the server 125 communicates, such as a server orcomputer.

Further, the claimed advancements may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with CPU 1100 and anoperating system such as Microsoft Windows, UNIX, Solaris, LINUX, AppleMAC-OS and other systems known to those skilled in the art.

The hardware elements in order to achieve the server 125 may be realizedby various circuitry elements, known to those skilled in the art. Forexample, CPU 1100 may be a Xenon or Core processor from Intel of Americaor an Opteron processor from AMD of America, or may be other processortypes that would be recognized by one of ordinary skill in the art. TheCPU 1100 may be implemented on an FPGA, ASIC, PLD or using discretelogic circuits, as one of ordinary skill in the art would recognize.Further, CPU 1100 may be implemented as multiple processorscooperatively working in parallel to perform the instructions of theinventive processes described above.

The server 125 in FIG. 14 also includes a network controller 1106, suchas an Intel Ethernet PRO network interface card from Intel Corporationof America, for interfacing with network 130. As can be appreciated, thenetwork 130 can be a public network, such as the Internet, or a privatenetwork such as an LAN or WAN network, or any combination thereof andcan also include PSTN or ISDN sub-networks. The network 130 can also bewired, such as an Ethernet network, or can be wireless such as acellular network including EDGE, 3G and 4G wireless cellular systems.The wireless network can also be Wi-Fi, Bluetooth, or any other wirelessform of communication that is known.

The server 125 further includes a display controller 1108, such as aNVIDIA GeForce GTX or Quadro graphics adaptor from NVIDIA Corporation ofAmerica for interfacing with display 1110, such as a Hewlett PackardHPL2445w LCD monitor. A general purpose I/O interface 1112 interfaceswith a keyboard and/or mouse 1114 as well as a touch screen panel 1116on or separate from display 1110. General purpose I/O interface alsoconnects to a variety of peripherals 1118 including printers andscanners, such as an OfficeJet or DeskJet from Hewlett Packard.

A sound controller 1120 is also provided in the server 125, such asSound Blaster X-Fi Titanium from Creative, to interface withspeakers/microphone 1122 thereby providing sounds and/or music.

The general purpose storage controller 1124 connects the storage mediumdisk 1104 with communication bus 1126, which may be an ISA, EISA, VESA,PCI, or similar, for interconnecting all of the components of the server125. A description of the general features and functionality of thedisplay 1110, keyboard and/or mouse 1114, as well as the displaycontroller 1108, storage controller 1124, network controller 1106, soundcontroller 1120, and general purpose I/O interface 1112 is omittedherein for brevity as these features are known.

Moreover, the present disclosure is not limited to the specific circuitelements described herein, nor is the present disclosure limited to thespecific sizing and classification of these elements. For example, theskilled artisan will appreciate that the circuitry described herein maybe adapted based on changes on battery sizing and chemistry, or based onthe requirements of the intended back-up load to be powered.

The functions and features described herein may also be executed byvarious distributed components of a system. For example, one or moreprocessors may execute these system functions, wherein the processorsare distributed across multiple components communicating in a network.The distributed components may include one or more client and servermachines, which may share processing, in addition to various humaninterface and communication devices (e.g., display monitors, smartphones, tablets, personal digital assistants (PDAs)). The network may bea private network, such as a LAN or WAN, or may be a public network,such as the Internet. Input to the system may be received via directuser input and received remotely either in real-time or as a batchprocess. Additionally, some implementations may be performed on modulesor hardware not identical to those described. Accordingly, otherimplementations are within the scope that may be claimed.

The above-described hardware description is a non-limiting example ofcorresponding structure for performing the functionality describedherein.

Having now described embodiments of the disclosed subject matter, itshould be apparent to those skilled in the art that the foregoing ismerely illustrative and not limiting, having been presented by way ofexample only. Thus, although particular configurations have beendiscussed herein, other configurations can also be employed. Numerousmodifications and other embodiments (e.g., combinations, rearrangements,etc.) are enabled by the present disclosure and are within the scope ofone of ordinary skill in the art and are contemplated as falling withinthe scope of the disclosed subject matter and any equivalents thereto.Features of the disclosed embodiments can be combined, rearranged,omitted, etc., within the scope of the invention to produce additionalembodiments. Furthermore, certain features may sometimes be used toadvantage without a corresponding use of other features. Accordingly,Applicant(s) intend(s) to embrace all such alternatives, modifications,equivalents, and variations that are within the spirit and scope of thedisclosed subject matter.

1. A system for reconstructing X-ray spectra, comprising: a measurementmask including at least one physical filter positioned between a sourcespectrum and an integrating detector to filter the source spectrum toproduce a masked spectrum; and processing circuitry configured togenerate a reconstructed spectrum based on applying a predeterminedalgorithm to measured data collected from the integrating detector andan identified configuration of the measurement mask.
 2. The system ofclaim 1, wherein the measurement mask includes a plurality of physicalfilters of a corresponding plurality of energy band-pass regions tofilter at least a portion of the masked spectrum.
 3. The system of claim2, wherein the plurality of physical filters is arranged in apseudo-random pattern to achieve an aggregate wide energy band-passregion of the measurement mask to cover energies of the source spectrum.4. The system of claim 2, wherein the plurality of physical filters isarranged corresponding to ordered mathematical combinations to achievean aggregate wide energy band-pass region of the measurement mask tocover energies of the source spectrum.
 5. The system of claim 1, whereinthe measurement mask includes a plurality of physical filters configuredand arranged to filter the masked spectrum according to a sub-pixelbasis, wherein a given sub-pixel of the measurement mask is smaller inarea than a given pixel of the integrating detector.
 6. The system ofclaim 5, wherein the plurality of physical filters of the measurementmask are arranged to provide four sub-pixels of filtering for each pixelof the integrating detector.
 7. The system of claim 1, wherein themeasurement mask provides the at least one physical filter comprising aplurality of metal foils, associated with corresponding pluralities ofenhanced X-ray energy absorption edges and attenuation values, arrangedin a two-dimensional grid corresponding to pixels of the integratingdetector.
 8. The system of claim 7, wherein the two-dimensional gridincludes at least one overlapping plurality of metal foils correspondingto providing an attenuation value of the at least one overlappingplurality of metal foils for at least a given sub-pixel of theintegrating detector.
 9. The system of claim 8, wherein the at least oneoverlapping plurality of metal foils corresponds to at least a givenpixel of the integrating detector.
 10. The system of claim 1, whereinthe predetermined algorithm is configured to perform a total variationminimization reconstruction.
 11. The system of claim 10, wherein thereconstructed spectrum is generated based on an iterative application ofthe total variation minimization reconstruction to iteratively checkwhether convergence of the reconstructed spectrum has been reached. 12.The system of claim 1, wherein the measurement mask is positionedbetween the source spectrum and the integrating detector to enable theintegrating detector to directly receive the masked spectrum duringoperation of the imaging system.
 13. The system of claim 12, wherein themeasurement mask is coupled to the integrating detector.
 14. The systemof claim 1, wherein the measurement mask is positioned between thesource spectrum and the integrating detector to enable the measurementmask to directly receive the source spectrum during operation of theimaging system.
 15. The system of claim 14, wherein the system furthercomprises a scintillator, and the measurement mask is coupled to thescintillator.
 16. The system of claim 1, wherein the system isconfigured to read a coded mask identification corresponding to aspecific X-ray source anode material of the source spectrum, anddetermine an identified configuration of the at least one physicalfilter corresponding to the coded mask identification, the identifiedconfiguration to be used as the measurement mask.
 17. A methodcomprising: identifying a type of X-ray source used in an imaging systemto produce a source spectrum; identifying a plurality of physicalfilters to provide a plurality of respective corresponding wide energyband-pass regions suitable to filter the source spectrum; identifying aconfiguration of the plurality of physical filters that when combinedinto a measurement mask achieve an aggregate wide energy band-passregion to cover energies of the source spectrum; filtering, by theimaging system, the source spectrum in accordance with the measurementmask, including the configuration of the plurality of physical filters,positioned between the source spectrum and an integrating detector ofthe imaging system, to filter the source spectrum to produce a maskedspectrum; and generating a reconstructed spectrum based on applying apredetermined algorithm to measured data that is collected from theintegrating detector, the predetermined algorithm accounting for theconfiguration of the plurality of physical filters.
 18. The method ofclaim 17, further comprising diagnosing a degradation of the X-raysource, based on identifying changes in the reconstructed spectrum,relative to a baseline characteristic of the X-ray source, over timewith repeated usage of the X-ray source.
 19. The method of claim 18,further comprising providing feedback, based on the diagnoseddegradation, that the X-ray source needs to be serviced.
 20. The methodof claim 17, further comprising identifying changes in the reconstructedspectrum in real-time, to detect changes in stability of imaged rapidlyevolving explosives.